ArcelorMittal turned to neural networks to improve real-time defect detection on the company's cold mills. The N2-D2 platform responded to the company's needs by automating neuromorphic circuit configuration and programming.

Neural networks are great at image and sound recognition. However, industrial users have had a hard time benefitting from the capabilities neural networks offer because of the high-level expertise it takes to implement them.

ArcelorMittal was seeking ways to improve the real-time image analysis system—used to detect surface defects—on its cold mills, and turned to List, a CEA Tech institute, for support. List researchers used the institute's new N2-D2 platform to automate the development of neuromorphic-circuit-based applications, facilitating design and programming. The researchers took advantage of the List-developed platform to test different neural network configurations and runtime environments to determine the best compromise between speed and performance—in record time.

It took less than three months to come up with a model of the optimal solution, which achieved the required level of performance, a 95% detection rate, while using minimal processing power. The application to be implemented is expected to achieve analysis speeds 130 times faster than traditional PCs.

N2-D2 also offers potential for the design of neural networks for Big Data and IoT applications.